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Evaluation of the Interindividual Human Variation in Bioactivation of Methyleugenol Using Physiologically Based Kinetic Modeling and Monte Carlo Simulations

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Specialties Pharmacology
Toxicology
Date 2015 Jan 1
PMID 25549870
Citations 7
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Abstract

The present study aims at predicting the level of formation of the ultimate carcinogenic metabolite of methyleugenol, 1'-sulfooxymethyleugenol, in the human population by taking variability in key bioactivation and detoxification reactions into account using Monte Carlo simulations. Depending on the metabolic route, variation was simulated based on kinetic constants obtained from incubations with a range of individual human liver fractions or by combining kinetic constants obtained for specific isoenzymes with literature reported human variation in the activity of these enzymes. The results of the study indicate that formation of 1'-sulfooxymethyleugenol is predominantly affected by variation in i) P450 1A2-catalyzed bioactivation of methyleugenol to 1'-hydroxymethyleugenol, ii) P450 2B6-catalyzed epoxidation of methyleugenol, iii) the apparent kinetic constants for oxidation of 1'-hydroxymethyleugenol, and iv) the apparent kinetic constants for sulfation of 1'-hydroxymethyleugenol. Based on the Monte Carlo simulations a so-called chemical-specific adjustment factor (CSAF) for intraspecies variation could be derived by dividing different percentiles by the 50th percentile of the predicted population distribution for 1'-sulfooxymethyleugenol formation. The obtained CSAF value at the 90th percentile was 3.2, indicating that the default uncertainty factor of 3.16 for human variability in kinetics may adequately cover the variation within 90% of the population. Covering 99% of the population requires a larger uncertainty factor of 6.4. In conclusion, the results showed that adequate predictions on interindividual human variation can be made with Monte Carlo-based PBK modeling. For methyleugenol this variation was observed to be in line with the default variation generally assumed in risk assessment.

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